In most manufacturing environments, reactive maintenance and quality control strategies are used to fix problems as they occur. Examples of such reactive strategies include statistical process control (SPC) and advanced process control (APC). A disadvantage of reactive strategies is that they respond to problems after such problems have already occurred. Thus, product may be scrapped, machines may be taken out of commission, employee man hours may be occupied, etc. as a result of the problem.
To prevent problems before they occur, some manufacturing environments implement predictive strategies. One such predictive strategy is to simulate future values of parameters. However, the visualizations, software and/or hardware implementations, etc. of the simulation of such future values are separate and distinct from those that show past and present values of the manufacturing environment. Such simulations are therefore not integrated with information available at the factory floor, and use different data than what is available at the factory floor. If an engineer desires to see predicted future values, he must execute a first application that provides such predictions. However, if an engineer desires to see past or present values, he must execute a separate second application that provides such information. Usually, a simulation application presents data in a different manner and with different controls than those used by an application that presents past and present values.
Simulation solutions also suffer from the use of models that are static approximations of the systems that they simulate at a specified point in time. Therefore, the results that such simulations produce in problem investigation are often based on obsolete or old data. While such simulations may still be valuable, they are often not trusted, and therefore are used only as supplementary information rather than true predictors.